I'm a radio astronomer and spend much of my working life manipulating datasets with AIPS and ParselTongue. Since 2011 I have been responsible for developing and maintaining the e-MERLIN data reduction pipeline, a script to automate the loading and calibration of data observed with the UK's array of radio telescopes. Here you can find links to the pipeline scripts, along with some associated utilities and documentation, and some other scripts which I've written which others may find useful.

e-MERLIN utilities

Below are some useful scripts I wrote to speed up loading and processing of data taken with the e-MERLIN system. I spent the summer of 2011 working at Jodrell Bank Centre for Astrophysics, helping with e-MERLIN commissioning and early science observations (here's an old article about the upgrade). I got fed up of loading the data by hand, so I decided to script it*. By the end of the summer I also had a working data reduction pipeline. While the pipeline is still in active development, the latest working version is provided with the caveat that it is not yet perfect and cannot yet cope with all observing modes and correlator idiosyncrasies.

To use these scripts with your data, you will need a working version of AIPS (tested with 31DEC11), as well as full installations of both Python (tested with version 2.7) and ParselTongue (tested with version 2.0).

The current working version of the full pipeline can now be obtained from the e-MERLIN observing pages. Contains the task definitions, a copy of the SERPent autoflagger, an L-band RFI flag mask, an example inputs file, and the script itself. This version should, for simple datasets, be able to load, sort, average, flag, concatenate, calibrate and image your e-MERLIN data. It is not perfect, and things will go wrong, so use at your own risk.

The current public release (beta) is v0.7 (doi:10.5281/zenodo.10163). If you use it, please consider an acknowledgement when you publish. Feedback to mkargo at manchester.

We are working on the documentation for e-MERLIN data reduction, and the pipeline will be described more fully there.